Search Results (6,410)

The Mediterranean faces frequent heavy precipitation, deadly heatwaves, and wildfires fueled by its climate. Greece, with its complex topography, experiences severe and extreme weather events that have escalated in recent years and are projected to continue rising under future climate conditions. This paper analyzes severe weather events and trends in Greece from 2010 to 2023, leveraging data from an expanded network of weather stations spanning across Greece, as well as long-term meteorological data from the reference weather station in the center of Athens. The focus includes analysis of heat waves, intense rainfall and droughts, thunderstorms, hail, tornadoes, and fire weather conditions. The societal impact of severe weather events is also discussed. The paper aims to provide both long-term (1901–2023) and recent year analyses (2010–2023). The main results show that between 2010 and 2023, Greece experienced: nearly one heatwave per summer; heavy rainfall events, most common in winter and autumn, showing a significant increase, particularly in the eastern Aegean and western continental Greece; dry spells, which are longest in southern Greece; thunderstorm and hail events peaking in spring and summer; fire weather conditions and risk peaking in southern Greece. Finally, societal impacts from weather hazards have increased in Greece over the past 14 years, with flash floods being the most frequent and damaging events, while public preparedness and effective risk communication remain low. Full article

In modern ocean exploration, forward-looking sonar (FLS) provides real-time 2D imaging of the seabed ahead, but its detection range is relatively limited. Conversely, side-scan sonar (SSS) enables large-scale imaging of the seabed during movement but struggles to effectively image areas directly beneath the sensor. Integrating FLS and SSS offers a promising solution by leveraging their complementary strengths to achieve comprehensive seabed mapping. However, no prior research has explored this fusion approach. This paper presents a novel method for FLS and SSS fusion mapping. Firstly, a novel sonar image enhancement method based on equalization is proposed, enabling simultaneous enhancement and grayscale unification of two sonar images. Additionally, an effective area extraction approach for FLS images, grounded on the approximate erosion method, is introduced to produce high-quality FLS mapping. Furthermore, by examining the data distribution in FLS and SSS mappings, the standard deviation of these datasets is utilized to refine the grayscale distribution of FLS mapping, thereby enhancing the grayscale distribution similarity between the two mapping results. Finally, FLS map data are seamlessly integrated into the gaps of the SSS map, resulting in a fused, comprehensive seabed representation. Large-scale experiments demonstrate that the proposed method effectively combines the strengths of FLS and SSS, producing complete and detailed seabed topography maps. Simultaneously, numerous ablation experiments are conducted to evaluate the impact of various parameters on fusion mapping, providing guidelines for selecting the optimal parameters. This fusion approach, thus, holds significant practical value for ocean exploration and seabed mapping applications. Full article

Insect pest infestations can vary due to spatial differences in microclimates and food availability within agroecosystems. Covariates can reflect these environmental conditions. This study tested whether using environmental covariates in two-phase sample optimization improved the spatial predictions for soybean insect pests. During the 2021–2022 crop season, insect pest samples were collected at 50 georeferenced points in a commercial soybean field in Brazil, alongside data on environmental covariates such as vegetation indices, soil properties, terrain topography, and distances from riparian areas. Three covariates were selected using correlation and principal component analysis (PCA). In the 2022–2023 crop season, sample designs were optimized using the iterative algorithm optimization of sample configurations using spatial simulated annealing (SPSANN) using the selected covariates, resulting in two optimized designs that were compared to a regular grid. Data from the three sampling designs comprising 50 points were evaluated using geostatistical methods, regression analysis (pest abundance), and classification (pest presence or absence) via the random forest algorithm. The data showed no spatial dependence, making using geostatistical interpolators inappropriate. However, a multi-objective optimized sampling design, tailored to refine configurations for identifying and estimating variograms and spatial trends essential for spatial interpolation, produced the most accurate predictions. Therefore, a two-phase sample optimization with prior in situ selection of environmental covariates improves pest predictions in agricultural systems, contributing to more efficient and sustainable agricultural management. Full article

Background/Objectives: To report the cosmetic, clinical, and visual outcomes of a combined surgical approach for treating a corneal/limbal dermoid using excision and a three-layered amniotic membrane graft with fibrin glue. Methods: An 18-year-old female presented with impaired vision and ocular discomfort caused by a prominent dome-shaped limbal congenital dermoid on the inferotemporal cornea, resulting in a significant aesthetic concern. A full assessment, including refraction, best-corrected visual acuity (BCVA), corneal topography, aberrometry and anterior segment OCT (AS-OCT) was conducted to plan the surgical approach. The dermoid was excised under peribulbar anaesthesia using manual lamellar dissection, followed by the application of 0.02% Mitomycin C and a multilayered amniotic membrane graft with fibrin glue. A bandage contact lens was applied and removed after three weeks, with postoperative treatment including topical antibiotics and steroids. Follow-ups were conducted on day 1, at 1 week, 3 weeks, 2 months, 6 months, 1 year, and 2 years. Results: Histopathological examination confirmed the mesoblastic nature of the lesion. Significant improvements in BCVA and ocular symptoms were observed. Corneal topography showed ocular surface regularization with reduction of high order aberrations and point spread function. AS-OCT showed complete integration of the amniotic membrane, with full epithelial coverage of the defect. The healing process was uneventful and the ocular surface remained stable throughout the entire follow-up, without complications or recurrence. Conclusions: This approach of dermoid excision, multilayered amniotic membrane and fibrin glue restored vision effectively, with notable improvements in ocular surface and cosmetic outcomes, without recurrence over two years. Full article

In this work, a study was carried out on the effect of temperature as the main influencing factor on the rheological behavior of lateritic suspensions, raw material for the operations of pressurized acid technology (HPAL) used to obtain nickel and cobalt from of oxidized ores. From studies of X-ray diffraction, X-ray fluorescence, particle size analyzer, and mathematical modeling, the behavior of the interactions and rheological characteristics of the analyzed samples were obtained. In this study, it was concluded that the use of mathematical models that relate the temperature up to 90 °C and the energy parameters of the pumping system of flows, loads, hydraulic losses, power, and efficiency would allow finding ways to increase and stabilize the flow of fed hydromixture with a flow rate of 1600 m³/h and a solids concentration of 48% (w/w) and guarantee the efficiency of the technological process. Full article

This study aims to identify the key factors contributing to the destabilization of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley and provide a robust scientific basis for proactive disaster prevention, management of disaster chains, and mitigation of multi-hazard clusters in unstable regions. The research focuses on the Yellow River–Huangshui River Valley, evaluating the stability of its geohazard disaster-pregnant environment. The disaster-pregnant environment is classified into static and dynamic categories. The static disaster-pregnant environment encompasses factors such as lithology, fracture density, topography, slope, river network density, and vegetation cover. The dynamic disaster-pregnant environment incorporates variables such as extreme rainfall, consecutive rainy days, annual rainfall averages, monthly high temperatures, monthly maximum temperature variations, average annual air temperatures, and human activities. A random forest model was employed to quantitatively assess the stability of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley. The findings indicated that (1) extreme indicators were the primary contributors to the destabilization of the disaster-pregnant environment, with very heavy rainfall contributing 28% and consecutive rainy days contributing 27%. Human activities ranked next, accounting for 15%. (2) Unstable regions for static, dynamic, and integrated disaster-pregnant environments accounted for 44%, 45%, and 44% of the study area, respectively, with all unstable areas concentrated in river valley regions. (3) The overall trend of stability in the disaster-pregnant environment was characterized by widespread instability. Extremely unstable areas were predominantly located in river valley regions, largely influenced by human activities. Conversely, only 0.1% of the region exhibited signs of stability, and 2.1% showed a tendency toward extreme stability. Full article

Herein, a WO₃@TCN photocatalyst was successfully synthesized using a self-assembly method, which demonstrated effectiveness in degrading organic dyestuffs and photocatalytic evolution of H₂. The synergistic effect between WO₃ and TCN, along with the porous structure of TCN, facilitated the formation of a heterojunction that promoted the absorption of visible light, accelerated the interfacial charge transfer, and inhibited the recombination of photogenerated electron–hole pairs. This led to excellent photocatalytic performance of 3%WO₃@TCN in degrading TC and catalyzing H₂ evolution from water splitting under visible-light irradiation. After modulation, the optimal 3%WO₃@TCN exhibited a maximal degradation rate constant that was twofold higher than that of TCN alone and showed continuous H₂ generation in the photocatalytic hydrogen evolution. Mechanistic studies revealed that •O₂⁻ constituted the major active species for the photocatalytic degradation of tetracycline. Experimental and DFT results verified the electronic transmission direction of WO₃@TCN heterojunction. Overall, this study facilitates the structural design of green TCN-based heterojunction photocatalysts and expands the application of TCN in the diverse photocatalytic processes. Additionally, this study offers valuable insights into strategically employing acid regulation modulation to enhance the performance of carbon nitride-based photocatalysts by altering the topography of WO₃@TCN composite material dramatically. Full article

Finding the relevant scale to observe the influence of a process is one of the most important purposes of multiscale surface characterization. This study investigates various methods to determine a pertinent scale for evaluating the relationship between the relative area and grit blasting pressure. Several media types were tested alongside two different methods for calculating the relative area and three bootstrapping approaches for scale determination through regression. Comparison with the existing literature highlights innovations in roughness parameter characterization, particularly the advantages of relative area over traditional parameters like Sa. This study also discusses the relevance of different media types in influencing surface topography. Additionally, insights from a similar study on the multiscale Sdq parameter and blasting pressure correlation are integrated, emphasizing a scale relevance akin to our Sdr method’s 120 µm cut-off length. Overall, our findings suggest a pertinent scale of 10,000 µm² for the Patchwork method and a 120 µm cut-off length for the Sdr method, derived from bootstrapping on residual regression across all media. At the relevant scale, every value of R² inferior to 0.83 is not significant with the threshold of 5% for the two methods of calculation of the relative area. This study enhances the understanding of how media types and blasting pressures impact surface topography, offering insights for refining material processing and surface treatment strategies. Full article

Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g., Light Detection and Ranging sensors (LIDARs) and stereo cameras. Stereo cameras produce dense point clouds with larger coverage but lower accuracy. LIDARs, on the other hand, have higher accuracy and longer range but much less coverage. LIDARs are also more expensive. The research question examines whether incorporating LIDARs can significantly improve stereo camera accuracy. Current sensor fusion methods use LIDARs’ raw measurements directly; thus, the improvement in estimation accuracy is limited to only LIDAR-scanned locations The main contribution of our new method is to construct a reference ground plane through the interpolation of LIDAR data so that the interpolated maps have similar coverage as the stereo camera’s point cloud. The interpolated maps are fused with the stereo camera point cloud via Kalman filters to improve a larger section of the topography map. The method is tested in three environments: controlled indoor, semi-controlled outdoor, and unstructured terrain. Compared to the existing method without LIDAR interpolation, the proposed approach reduces average error by 40% in the controlled environment and 67% in the semi-controlled environment, while maintaining large coverage. The unstructured environment evaluation confirms its corrective impact. Full article

Bioretention cells (BCs) are widely used to manage urban runoff due to their positive impact on runoff control. Current research primarily focuses on optimizing the internal structural design of bioretention cells, while studies on the interactions between their spatial configuration, topography, and land use types are limited. This study employs the Storm Water Management Model (SWMM) and uses extreme rainfall to analyze the influence of typical stormwater flow paths, determined by various land use types and topography, as well as the spatial configurations of bioretention cells on catchment hydrological performance. The results show the following: (1) Different stormwater flow paths significantly affect catchment hydrological performance, with series-type pathways performing best. (2) The spatial configuration of bioretention cells significantly influences catchment hydrological performance. Decentralized BCs under series-type pathways showed better performance for reducing total outflow and peak runoff, with reduction rates increasing by 7.1% and 8.8%, while centralized BCs better delayed peak times. (3) Stormwater flow paths affect BC efficiency in catchment hydrological performance. Decentralized BCs under a series-type stormwater flow path are recommended for priority use. This study provides a novel perspective for optimizing the spatial arrangement of BCs and urban stormwater management, thereby contributing to flood risk mitigation. Full article

Biomimetic patterning emerges as a promising antibiotic-free approach to protect medical devices from bacterial adhesion and biofilm formation. The main advantage of this approach lies in its simplicity and scalability for industrial applications. In this study, we employ it to produce antibacterial coatings based on silicone materials, widely used in the healthcare industry. In doing so, we patterned silicone substrates with a topography of various flower petals (rose, chamomile, pansy, and magnolia) and studied the relationship between the antibacterial properties of the obtained biomimetic substrates and their surface topography. To study the surface topography of biomimetic surfaces, we used the fractal analysis of their SEM images. In particular, as a measure of surface complexity and heterogeneity, we used the values of the developed interfacial area ratio (Sdr) and lacunarity coefficient (β). In the result, we found that the bacterial area coverage of biomimetic substrates decreased exponentially with the increase in their surface complexity and heterogeneity, and prominent antibacterial properties were observed at β > 1.6 and Sdr > 50. The results of this study can be used to identify biomimetic materials with superior antibacterial properties and produce efficient antibacterial silicone coatings for biomedical and healthcare applications. Full article

Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors’ influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation. Full article

The dune density is an important parameter for representing the characteristics of desert geomorphology, providing a precise depiction of the undulating topography of the desert. Owing to the limitations of estimation methods and data availability, accurately quantifying dune density has posed a significant challenge; in response to this issue, we propose an innovative model to estimate dune density using a dune vertex search combined with four-directional orographic spectral decomposition. This study reveals several key insights: (1) Taklimakan Desert distributes approximately 5.31 × 10⁷ dunes, with a linear regression fit R² of 0.79 between the estimated and observed values. The average absolute error and root mean square error are calculated as 25.61 n/km² and 30.48 n/km², respectively. (2) The distribution of dune density across the eastern, northeastern, southern, and western parts of the Taklimakan Desert is relatively lower, while there is higher dune density in the central and northern areas. (3) The observation data constructed using the improved YOLOv8s algorithm and remote sensing imagery effectively validate the estimation results of dune density. The new algorithm demonstrates a high level of accuracy in estimating sand dune density, thereby providing crucial parameters for sub-grid orographic parameterization in desert regions. Additionally, its application potential in dust modeling appears promising. Full article

The effectiveness of data-driven landslide susceptibility mapping relies on data integrity and advanced geospatial analysis; however, selecting the most suitable method and identifying key regional factors remains a challenging task. To address this, this study assessed the performance of six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF and CNN+CatBoost), and a Stacking Ensemble (SE) combining CNN, RF, and CatBoost in mapping landslide susceptibility along the Karakoram Highway in northern Pakistan. Twelve geospatial factors were examined, categorized into Topography/Geomorphology, Land Cover/Vegetation, Geology, Hydrology, and Anthropogenic Influence. A detailed landslide inventory of 272 occurrences was compiled to train the models. The proposed stacking ensemble and hybrid models improve landslide susceptibility modeling, with the stacking ensemble achieving an AUC of 0.91. Hybrid modeling enhances accuracy, with CNN–RF boosting RF’s AUC from 0.85 to 0.89 and CNN–CatBoost increasing CatBoost’s AUC from 0.87 to 0.90. Chi-square (χ²) values (9.8–21.2) and p-values (<0.005) confirm statistical significance across models. This study identifies approximately 20.70% of the area as from high to very high risk, with the SE model excelling in detecting high-risk zones. Key factors influencing landslide susceptibility showed slight variations across the models, while multicollinearity among variables remained minimal. The proposed modeling approach reduces uncertainties, enhances prediction accuracy, and supports decision-makers in implementing effective landslide mitigation strategies. Full article

Salem, Massachusetts, is one of the oldest cities in the United States (1629) and its coastal location on the Atlantic helped create one of the wealthiest cities in America during the late 18th century, but today its coastal location threatens many of its buildings due to sea level rise and increased storm activity. The House of the Seven Gables, a National Historic Landmark District, consists of five important historic buildings, the most famous being The Turner Ingersoll Mansion (1668), more commonly known as The House of the Seven Gables. Considered one of the most important houses in America, it is also one of the most threatened historic buildings due to its location on Salem’s harbor. The House of the Seven Gables conducted a two-year study funded by Massachusetts Coastal Zone Management to evaluate the risks posed by climate change. This process included the use of data from groundwater monitoring wells and a tidal gauge installed on-site, along with soil samples and a detailed survey base plan including topography and subsurface infrastructure. The project team then used the Massachusetts Coastal Flood Risk Model (MC-FRM) to assess climate change impacts on the site in 2030, 2050, and 2070, and then created a plan for adaptations that should be implemented before those risks materialize. Strategies for adapting to storm surges, increasing groundwater, and intense surface water runoff were evaluated for their effectiveness and appropriateness for the historic site. The conclusion of the study resulted in a five-phase plan ending in the managed retreat of the historic buildings to higher ground on the existing site. This article goes beyond other research that suggests coastal retreats by demonstrating how to quantitatively evaluate current and future coastal issues with predictive models and how to set viable dates for adaptive solutions and a managed retreat. Full article